Protective role of melatonin in breast cancer: what we can learn from women with blindness.

PURPOSE: This review proposes an overall vision of the protective and therapeutic role of melatonin in breast cancer: from the specific cases of blind women and their reduction of breast cancer incidence to all clinical uses of the sleep hormone in breast cancer. METHODS: We reviewed studies focused on (1) the correlation between blindness and breast cancer, (2) the correlation between melatonin and breast cancer occurrence in the general population, (3) melatonin therapeutic use in breast cancer, and (4) we discussed the properties of melatonin that could explain an anticancer effect. RESULTS: (1) Seven studies of breast cancer risk in blind women related significant incidence decreases, up to 57%, among totally blind women. The limited number of studies and the absence of adjustment for confounding factors in most studies limit conclusions. None of these studies established melatonin profiles to determine whether blind women with a decreased breast cancer incidence produced higher levels of melatonin. (2) In the general population, 5 meta-analyses and 12 prospective-cohort studies focused on melatonin levels at recruitment and breast cancer occurrence. All reported the absence of correlation in premenopausal women, whereas in postmenopausal women, most studies showed significantly decreased risk for women with highest melatonin levels. (3) The therapeutic interest of melatonin associated with chemotherapy, radiotherapy, and hormonotherapy is poorly documented in breast cancer to conclude on a positive effect. (4) Melatonin effects on mammary carcinogenesis were only reported in in vitro and animal studies that demonstrated antiestrogenic, antioxidant, oncostatic, and immunomodulatory properties. CONCLUSION: The preventive role of high endogenous melatonin on breast cancer as well as its beneficial therapeutic use remains to be proven.

A novel machine learning-derived decision tree including uPA/PAI-1 for breast cancer care.

Background uPA and PAI-1 are breast cancer biomarkers that evaluate the benefit of chemotherapy (CT) for HER2-negative, estrogen receptor-positive, low or intermediate grade patients. Our objectives were to observe clinical routine use of uPA/PAI-1 and to build a new therapeutic decision tree integrating uPA/PAI-1. Methods We observed the concordance between CT indications proposed by a canonical decision tree representative of French practices (not including uPA/PAI-1) and actual CT prescriptions decided by a medical board which included uPA/PAI-1. We used a method of machine learning for the analysis of concordant and non-concordant CT prescriptions to generate a novel scheme for CT indications. Results We observed a concordance rate of 71% between indications proposed by the canonical decision tree and actual prescriptions. Discrepancies were due to CT contraindications, high tumor grade and uPA/PAI-1 level. Altogether, uPA/PAI-1 were a decisive factor for the final decision in 17% of cases by avoiding CT prescription in two-thirds of cases and inducing CT in other cases. Remarkably, we noted that in routine practice, elevated uPA/PAI-1 levels seem not to be considered as a sufficient indication for CT for N≤3, Ki 67≤30% tumors, but are considered in association with at least one additional marker such as Ki 67>14%, vascular invasion and ER-H score <150. Conclusions This study highlights that in the routine clinical practice uPA/PAI-1 are never used as the sole indication for CT. Combined with other routinely used biomarkers, uPA/PAI-1 present an added value to orientate the therapeutic choice.

Relevance of breast MRI in determining the size and focality of invasive breast cancer treated by mastectomy: a prospective study.

BACKGROUND: The aim of this study was the evaluation of breast MRI in determining the size and focality of invasive non-metastatic breast cancers. METHODS: The prospective, single-centre study conducted in 2015 compared preoperative MRI with histological analysis of mastectomy. RESULTS: One hundred one mastectomies from 98 patients were extensively analysed. The rates of false-positive and false-negative MRI were 2 and 4% respectively. The sensitivity of breast MRI was 84.7% for the detection of all invasive foci, 69% for single foci and 65.7% for multiple foci. In the evaluation of tumour size, the Spearman rank correlation coefficient r between the sizes obtained by MRI and histology was 0.62. The MRI-based prediction of a complete response to neoadjuvant chemotherapy was 75%. DISCUSSION: MRI exhibits high sensitivity in the detection of invasive breast cancers. False positives were linked to the inflammatory nature of the tumour bed. False negatives were associated with small or low-grade tumours and their retro-areolar location. The size of T1 tumours was overestimated by an average of 7%, but MRI was the most efficient procedure. The sensitivity of MRI for the diagnosis of unifocal tumours was higher than that for multifocal sites. Our study confirmed the positive contribution of preoperative MRI for invasive lobular carcinomas and complete response predictions after neoadjuvant chemotherapy.

A prospective study to assess the clinical utility of serum HER2 extracellular domain in breast cancer with HER2 overexpression.

PURPOSE: We explored the clinical utility of human epidermal growth factor receptor-2 extracellular domain (HER2/ECD) in patients treated for an invasive breast cancer with HER2 overexpression. METHODS: We prospectively studied HER2/ECD levels in the sera of 334 women included between 2007 and 2014, all treated with trastuzumab. HER2/ECD levels were measured at diagnosis, during treatments, and along the follow-up. We investigated the relationship of HER2/ECD with other clinicopathological parameters at diagnosis, its prognosis value, and its utility during the monitoring of a neoadjuvant treatment and the follow-up. RESULTS: Elevated HER2/ECD at diagnosis correlated positively with parameters associated with tumor aggressiveness. Disease-free survival of non-metastatic patients was significantly shorter in patients with high HER2/ECD at diagnosis (HR = 13.6, 95 % CI 1.6-113.6, P < 0.0001). Progression-free survival of metastatic patients was better for patients with low HER2/ECD (HR = 2.6, 95 % CI 1.2-5.3, P = 0.033). A multivariate analysis revealed that HER2/ECD level at diagnosis was an independent prognosis factor. During neoadjuvant therapy, a significant decrease in HER2/ECD was reported only for the complete histological response group (P = 0.031). During the follow-up, HER2/ECD helped predict relapse, disease progression, and metastases before imaging in 18.6 % cases of the studied cohort. CONCLUSIONS: HER2/ECD is a prognosis factor that is valuable in evaluating the neoadjuvant treatment efficiency. HER2/ECD also appears to be a helpful surveillance biomarker for the early diagnosis of relapses and to predict the fate of metastases. This study brings evidences to support the use of HER2/ECD in the management of HER2-positive breast cancer.

From free-text electronic health records to structured cohorts: Onconum, an innovative methodology for real-world data mining in breast cancer.

PURPOSE: A considerable amount of valuable information is present in electronic health records (EHRs) however it remains inaccessible because it is embedded into unstructured narrative documents that cannot be easily analyzed. We wanted to develop and evaluate a methodology able to extract and structure information from electronic health records in breast cancer. METHODS: We developed a software platform called Onconum (ClinicalTrials.gov Identifier: NCT02810093) which uses a hybrid method relying on machine learning approaches and rule-based lexical methods. It is based on natural language processing techniques that allows a targeted analysis of free-text medical data related to breast cancer, independently of any pre-existing dictionary, in a French context (available in N files). We then evaluated it on a validation cohort called Senometry. FINDINGS: Senometry cohort included 9,599 patients with breast cancer (both invasive and in situ), treated between 2000 and 2017 in the breast cancer unit of Strasbourg University Hospitals. Extraction rates ranged from 45 to 100%, depending on the type of each parameter. Precision of extracted information was 68%-94% compared to a structured cohort, and 89%-98% compared to manually structured databases and it retrieved more rare occurrences compared to another database search engine (+17%). INTERPRETATION: This innovative method can accurately structure relevant medical information embedded in EHRs in the context of breast cancer. Missing data handling is the main limitation of this method however multiple sources can be incorporated to reduce this limit. Nevertheless, this methodology does not need neither pre-existing dictionaries nor manually annotated corpora. It can therefore be easily implemented in non-English-speaking countries and in other diseases outside breast cancer, and it allows prospective inclusion of new patients.